Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning-Reference-Cited by-同舟云学术

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Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning

Published:2018-10-09 Issue:4 Volume:29 Page:1961-1967
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Liang Shujun,Tang Fan,Huang Xia,Yang Kaifan,Zhong Tao,Hu Runyue,Liu Shangqing,Yuan Xinrui,Zhang Yu

Funder

National Natural Science Foundation of China

The Science and Technology Program of Guangdong Province

The Guangdong Provincial Key Laboratory of Medical Image Processing

The Science and Technology Program of Guangzhou

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

http://link.springer.com/article/10.1007/s00330-018-5748-9/fulltext.html

Reference26 articles.

1. Mohammed MA, Ghani MKA, Hamed RI, Ibrahim DA (2017) Review on nasopharyngeal carcinoma: concepts, methods of analysis, segmentation, classification, prediction and impact: a review of the research literature. J Comput Sci 21:283–298

2. Brouwer CL, Steenbakkers RJ, Heuvel EVD et al (2012) 3D variation in delineation of head and neck organs at risk. Radiat Oncol 7(1):1–10

3. Weiss E, Hess CF (2003) The impact of gross tumor volume (GTV) and clinical target volume (CTV) definition on the total accuracy in radiotherapy theoretical aspects and practical experiences. Strahlenther Onkol 179(1):21–30

4. Faggiano E, Fiorino C, Scalco E et al (2011) An automatic contour propagation method to follow parotid gland deformation during head-and-neck cancer tomotherapy. Phys Med Biol 56(3):775–791

5. Rueckert D, Frangi AF, Schnabel JA (2003) Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration. IEEE Trans Med Imaging 22(8):1014–1025

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